Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations17520
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory72.0 B

Variable types

Numeric9

Alerts

boundary_layer_height is highly overall correlated with surface_solar_radiation_downwards and 1 other fieldsHigh correlation
dewpoint_temperature_2m is highly overall correlated with surface_pressure and 1 other fieldsHigh correlation
surface_pressure is highly overall correlated with dewpoint_temperature_2m and 1 other fieldsHigh correlation
surface_solar_radiation_downwards is highly overall correlated with boundary_layer_heightHigh correlation
temperature_2m is highly overall correlated with boundary_layer_height and 2 other fieldsHigh correlation
u_component_of_wind_100m is highly overall correlated with v_component_of_wind_100mHigh correlation
v_component_of_wind_100m is highly overall correlated with u_component_of_wind_100mHigh correlation
boundary_layer_height has unique values Unique
dewpoint_temperature_2m has unique values Unique
temperature_2m has unique values Unique
u_component_of_wind_100m has unique values Unique
v_component_of_wind_100m has unique values Unique

Reproduction

Analysis started2025-10-04 18:34:07.228373
Analysis finished2025-10-04 18:34:12.757381
Duration5.53 seconds
Software versionydata-profiling v4.16.1
Download configurationconfig.json

Variables

boundary_layer_height
Real number (ℝ)

High correlation  Unique 

Distinct17520
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2444874 × 10-18
Minimum-0.72177345
Maximum6.3788689
Zeros0
Zeros (%)0.0%
Negative11891
Negative (%)67.9%
Memory size137.0 KiB
2025-10-05T00:04:12.799070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.72177345
5-th percentile-0.7032913
Q1-0.64721762
median-0.44429459
Q30.28724241
95-th percentile2.0070813
Maximum6.3788689
Range7.1006423
Interquartile range (IQR)0.93446002

Descriptive statistics

Standard deviation1.0000285
Coefficient of variation (CV)3.082239 × 1017
Kurtosis6.5196864
Mean3.2444874 × 10-18
Median Absolute Deviation (MAD)0.24145902
Skewness2.3371139
Sum-1.1368684 × 10-13
Variance1.0000571
MonotonicityNot monotonic
2025-10-05T00:04:12.868331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.6738551498 1
 
< 0.1%
-0.6631214379 1
 
< 0.1%
-0.6578295895 1
 
< 0.1%
-0.6567843709 1
 
< 0.1%
-0.625886568 1
 
< 0.1%
-0.5389239877 1
 
< 0.1%
-0.3147116392 1
 
< 0.1%
0.1440242246 1
 
< 0.1%
0.3484058558 1
 
< 0.1%
0.4763855952 1
 
< 0.1%
Other values (17510) 17510
99.9%
ValueCountFrequency (%)
-0.7217734507 1
< 0.1%
-0.7217667941 1
< 0.1%
-0.7216489365 1
< 0.1%
-0.7215177401 1
< 0.1%
-0.7215089763 1
< 0.1%
-0.7214432607 1
< 0.1%
-0.7214254801 1
< 0.1%
-0.7213405758 1
< 0.1%
-0.7213392217 1
< 0.1%
-0.7212300442 1
< 0.1%
ValueCountFrequency (%)
6.378868855 1
< 0.1%
6.192652023 1
< 0.1%
6.132717226 1
< 0.1%
6.117610888 1
< 0.1%
6.054728234 1
< 0.1%
6.016579526 1
< 0.1%
5.927229322 1
< 0.1%
5.898028937 1
< 0.1%
5.833711175 1
< 0.1%
5.797264596 1
< 0.1%

dewpoint_temperature_2m
Real number (ℝ)

High correlation  Unique 

Distinct17520
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.5047443 × 10-15
Minimum-2.8874315
Maximum1.7978559
Zeros0
Zeros (%)0.0%
Negative9273
Negative (%)52.9%
Memory size137.0 KiB
2025-10-05T00:04:12.935319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-2.8874315
5-th percentile-1.4828941
Q1-0.81457887
median-0.09159358
Q31.0181691
95-th percentile1.4606651
Maximum1.7978559
Range4.6852874
Interquartile range (IQR)1.8327479

Descriptive statistics

Standard deviation1.0000285
Coefficient of variation (CV)-3.9925375 × 1014
Kurtosis-1.1312791
Mean-2.5047443 × 10-15
Median Absolute Deviation (MAD)0.87627839
Skewness-0.010304702
Sum-4.5190518 × 10-11
Variance1.0000571
MonotonicityNot monotonic
2025-10-05T00:04:12.998119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.515919565 1
 
< 0.1%
-1.94152445 1
 
< 0.1%
-1.933410526 1
 
< 0.1%
-1.927168279 1
 
< 0.1%
-1.931126802 1
 
< 0.1%
-1.802173254 1
 
< 0.1%
-1.312108318 1
 
< 0.1%
-1.295229789 1
 
< 0.1%
-1.292017835 1
 
< 0.1%
-1.293523778 1
 
< 0.1%
Other values (17510) 17510
99.9%
ValueCountFrequency (%)
-2.887431501 1
< 0.1%
-2.865228926 1
< 0.1%
-2.84572529 1
< 0.1%
-2.824990724 1
< 0.1%
-2.797311964 1
< 0.1%
-2.789134343 1
< 0.1%
-2.665982573 1
< 0.1%
-2.610842833 1
< 0.1%
-2.608540941 1
< 0.1%
-2.590255944 1
< 0.1%
ValueCountFrequency (%)
1.797855888 1
< 0.1%
1.785342771 1
< 0.1%
1.778405255 1
< 0.1%
1.768092263 1
< 0.1%
1.763394123 1
< 0.1%
1.7534977 1
< 0.1%
1.74928748 1
< 0.1%
1.737649581 1
< 0.1%
1.736651477 1
< 0.1%
1.736127128 1
< 0.1%

surface_pressure
Real number (ℝ)

High correlation 

Distinct17519
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0083867 × 10-14
Minimum-2.3415405
Maximum2.6692535
Zeros0
Zeros (%)0.0%
Negative9042
Negative (%)51.6%
Memory size137.0 KiB
2025-10-05T00:04:13.058093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-2.3415405
5-th percentile-1.5034445
Q1-0.86587487
median-0.067654705
Q30.90046127
95-th percentile1.492639
Maximum2.6692535
Range5.010794
Interquartile range (IQR)1.7663361

Descriptive statistics

Standard deviation1.0000285
Coefficient of variation (CV)9.9171138 × 1013
Kurtosis-1.167969
Mean1.0083867 × 10-14
Median Absolute Deviation (MAD)0.89103994
Skewness0.031590547
Sum1.7598722 × 10-10
Variance1.0000571
MonotonicityNot monotonic
2025-10-05T00:04:13.122397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2316978567 2
 
< 0.1%
-0.1347266265 1
 
< 0.1%
0.2323963726 1
 
< 0.1%
0.3240813121 1
 
< 0.1%
0.3227499669 1
 
< 0.1%
0.2553815869 1
 
< 0.1%
0.07656415238 1
 
< 0.1%
0.0006115375318 1
 
< 0.1%
-0.1318155963 1
 
< 0.1%
-0.2206874951 1
 
< 0.1%
Other values (17509) 17509
99.9%
ValueCountFrequency (%)
-2.341540461 1
< 0.1%
-2.312334189 1
< 0.1%
-2.287239265 1
< 0.1%
-2.254045982 1
< 0.1%
-2.231734133 1
< 0.1%
-2.227675005 1
< 0.1%
-2.222386203 1
< 0.1%
-2.157089144 1
< 0.1%
-2.156780963 1
< 0.1%
-2.155646388 1
< 0.1%
ValueCountFrequency (%)
2.669253512 1
< 0.1%
2.633528344 1
< 0.1%
2.568626931 1
< 0.1%
2.524908814 1
< 0.1%
2.513238143 1
< 0.1%
2.468200923 1
< 0.1%
2.464119222 1
< 0.1%
2.4283659 1
< 0.1%
2.419515822 1
< 0.1%
2.407563533 1
< 0.1%

surface_solar_radiation_downwards
Real number (ℝ)

High correlation 

Distinct9477
Distinct (%)54.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3626847 × 10-16
Minimum-0.74303293
Maximum2.7932951
Zeros0
Zeros (%)0.0%
Negative11233
Negative (%)64.1%
Memory size137.0 KiB
2025-10-05T00:04:13.181161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.74303293
5-th percentile-0.74303149
Q1-0.74303137
median-0.70709244
Q30.74158472
95-th percentile2.0216269
Maximum2.7932951
Range3.536328
Interquartile range (IQR)1.4846161

Descriptive statistics

Standard deviation1.0000285
Coefficient of variation (CV)7.3386642 × 1015
Kurtosis-0.32039059
Mean1.3626847 × 10-16
Median Absolute Deviation (MAD)0.035939042
Skewness1.0489339
Sum3.4106051 × 10-12
Variance1.0000571
MonotonicityNot monotonic
2025-10-05T00:04:13.240877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.7430313658 5389
30.8%
-0.7430314865 1192
 
6.8%
-0.7430312452 1186
 
6.8%
-0.7430311246 141
 
0.8%
-0.7430316071 137
 
0.8%
-0.7429751449 2
 
< 0.1%
-0.742832708 2
 
< 0.1%
-0.7426399115 2
 
< 0.1%
1.824582068 1
 
< 0.1%
1.529415802 1
 
< 0.1%
Other values (9467) 9467
54.0%
ValueCountFrequency (%)
-0.7430329342 1
 
< 0.1%
-0.7430316071 137
 
0.8%
-0.7430314865 1192
 
6.8%
-0.7430313658 1
 
< 0.1%
-0.7430313658 5389
30.8%
-0.7430313658 1
 
< 0.1%
-0.7430312452 1186
 
6.8%
-0.7430311246 141
 
0.8%
-0.74301103 1
 
< 0.1%
-0.7430105927 1
 
< 0.1%
ValueCountFrequency (%)
2.793295071 1
< 0.1%
2.766271647 1
< 0.1%
2.765683253 1
< 0.1%
2.741338096 1
< 0.1%
2.73371558 1
< 0.1%
2.731879692 1
< 0.1%
2.71933392 1
< 0.1%
2.710369464 1
< 0.1%
2.709596973 1
< 0.1%
2.703710409 1
< 0.1%

temperature_2m
Real number (ℝ)

High correlation  Unique 

Distinct17520
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.4275744 × 10-15
Minimum-2.6274645
Maximum2.3510254
Zeros0
Zeros (%)0.0%
Negative7641
Negative (%)43.6%
Memory size137.0 KiB
2025-10-05T00:04:13.302637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-2.6274645
5-th percentile-1.7656873
Q1-0.73754716
median0.2064274
Q30.71911005
95-th percentile1.4684613
Maximum2.3510254
Range4.9784899
Interquartile range (IQR)1.4566572

Descriptive statistics

Standard deviation1.0000285
Coefficient of variation (CV)-7.0050885 × 1014
Kurtosis-0.66650086
Mean-1.4275744 × 10-15
Median Absolute Deviation (MAD)0.68231878
Skewness-0.3182343
Sum-2.3419489 × 10-11
Variance1.0000571
MonotonicityNot monotonic
2025-10-05T00:04:13.363818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.993947401 1
 
< 0.1%
-2.247569391 1
 
< 0.1%
-2.262847865 1
 
< 0.1%
-2.236287001 1
 
< 0.1%
-2.220800378 1
 
< 0.1%
-2.058698421 1
 
< 0.1%
-1.347899377 1
 
< 0.1%
-1.109739219 1
 
< 0.1%
-1.029243147 1
 
< 0.1%
-0.715057983 1
 
< 0.1%
Other values (17510) 17510
99.9%
ValueCountFrequency (%)
-2.627464495 1
< 0.1%
-2.619565327 1
< 0.1%
-2.613391909 1
< 0.1%
-2.591939884 1
< 0.1%
-2.588246657 1
< 0.1%
-2.587371227 1
< 0.1%
-2.58602718 1
< 0.1%
-2.583506046 1
< 0.1%
-2.572805486 1
< 0.1%
-2.571522021 1
< 0.1%
ValueCountFrequency (%)
2.351025417 1
< 0.1%
2.345202334 1
< 0.1%
2.339569758 1
< 0.1%
2.336561386 1
< 0.1%
2.325949528 1
< 0.1%
2.325891359 1
< 0.1%
2.321584575 1
< 0.1%
2.305864012 1
< 0.1%
2.305103364 1
< 0.1%
2.304984512 1
< 0.1%

total_cloud_cover
Real number (ℝ)

Distinct9788
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.038236 × 10-16
Minimum-0.86875578
Maximum1.7479252
Zeros0
Zeros (%)0.0%
Negative10837
Negative (%)61.9%
Memory size137.0 KiB
2025-10-05T00:04:13.422620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.86875578
5-th percentile-0.86875578
Q1-0.86875578
median-0.51215967
Q30.94171129
95-th percentile1.7479252
Maximum1.7479252
Range2.616681
Interquartile range (IQR)1.8104671

Descriptive statistics

Standard deviation1.0000285
Coefficient of variation (CV)9.6319967 × 1015
Kurtosis-1.1255954
Mean1.038236 × 10-16
Median Absolute Deviation (MAD)0.35659612
Skewness0.71983355
Sum-3.1832315 × 10-12
Variance1.0000571
MonotonicityNot monotonic
2025-10-05T00:04:13.483793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8687557847 5375
30.7%
1.74792519 919
 
5.2%
-0.8679163043 64
 
0.4%
1.747085738 57
 
0.3%
-0.8653956984 50
 
0.3%
-0.8587264339 47
 
0.3%
-0.8671057422 47
 
0.3%
1.737031607 37
 
0.2%
-0.857862251 36
 
0.2%
1.737895889 32
 
0.2%
Other values (9778) 10856
62.0%
ValueCountFrequency (%)
-0.8687557847 5375
30.7%
-0.8679163043 64
 
0.4%
-0.867684703 8
 
< 0.1%
-0.8671057422 47
 
0.3%
-0.8668452225 7
 
< 0.1%
-0.8666425961 6
 
< 0.1%
-0.8662662617 27
 
0.2%
-0.8660346887 2
 
< 0.1%
-0.8658030874 2
 
< 0.1%
-0.8654267813 15
 
0.1%
ValueCountFrequency (%)
1.74792519 919
5.2%
1.747085738 57
 
0.3%
1.746854109 11
 
0.1%
1.746275119 24
 
0.1%
1.7460146 15
 
0.1%
1.745811973 2
 
< 0.1%
1.745435667 15
 
0.1%
1.745204094 2
 
< 0.1%
1.744596215 7
 
< 0.1%
1.744565054 23
 
0.1%

total_precipitation
Real number (ℝ)

Distinct4531
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.8933849 × 10-17
Minimum-0.19808525
Maximum30.85581
Zeros0
Zeros (%)0.0%
Negative15648
Negative (%)89.3%
Memory size137.0 KiB
2025-10-05T00:04:13.545339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.19808525
5-th percentile-0.19807915
Q1-0.19807712
median-0.19807712
Q3-0.19728904
95-th percentile0.73030255
Maximum30.85581
Range31.053895
Interquartile range (IQR)0.00078808291

Descriptive statistics

Standard deviation1.0000285
Coefficient of variation (CV)-2.5685325 × 1016
Kurtosis197.77984
Mean-3.8933849 × 10-17
Median Absolute Deviation (MAD)0
Skewness11.192522
Sum-2.3874236 × 10-12
Variance1.0000571
MonotonicityNot monotonic
2025-10-05T00:04:13.606817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1980771183 8790
50.2%
-0.1980791502 1535
 
8.8%
-0.1980750865 1527
 
8.7%
-0.198081182 569
 
3.2%
-0.1980730547 541
 
3.1%
-0.1980852457 10
 
0.1%
-0.198068991 8
 
< 0.1%
-0.1966419027 2
 
< 0.1%
-0.1977302146 2
 
< 0.1%
-0.1974670222 2
 
< 0.1%
Other values (4521) 4534
25.9%
ValueCountFrequency (%)
-0.1980852457 10
 
0.1%
-0.198081182 569
 
3.2%
-0.1980791502 1535
 
8.8%
-0.1980784729 1
 
< 0.1%
-0.1980781343 1
 
< 0.1%
-0.1980777956 1
 
< 0.1%
-0.1980771183 2
 
< 0.1%
-0.1980771183 1
 
< 0.1%
-0.1980771183 1
 
< 0.1%
-0.1980771183 8790
50.2%
ValueCountFrequency (%)
30.85580951 1
< 0.1%
28.31449236 1
< 0.1%
26.58815623 1
< 0.1%
21.71458644 1
< 0.1%
20.11405104 1
< 0.1%
16.79755509 1
< 0.1%
15.95002722 1
< 0.1%
15.4156945 1
< 0.1%
15.26128304 1
< 0.1%
14.50985491 1
< 0.1%

u_component_of_wind_100m
Real number (ℝ)

High correlation  Unique 

Distinct17520
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.6778529 × 10-17
Minimum-3.5300849
Maximum2.9281054
Zeros0
Zeros (%)0.0%
Negative8052
Negative (%)46.0%
Memory size137.0 KiB
2025-10-05T00:04:13.667319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-3.5300849
5-th percentile-1.7329229
Q1-0.7096748
median0.11150152
Q30.71743582
95-th percentile1.5217059
Maximum2.9281054
Range6.4581903
Interquartile range (IQR)1.4271106

Descriptive statistics

Standard deviation1.0000285
Coefficient of variation (CV)-1.7612794 × 1016
Kurtosis-0.3425226
Mean-5.6778529 × 10-17
Median Absolute Deviation (MAD)0.69428952
Skewness-0.24953053
Sum-6.8212103 × 10-13
Variance1.0000571
MonotonicityNot monotonic
2025-10-05T00:04:13.728051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3071684689 1
 
< 0.1%
0.9867859999 1
 
< 0.1%
0.9981671981 1
 
< 0.1%
0.9478275884 1
 
< 0.1%
0.8078391284 1
 
< 0.1%
0.4470172475 1
 
< 0.1%
0.3009480803 1
 
< 0.1%
0.3492305336 1
 
< 0.1%
0.3360370405 1
 
< 0.1%
0.2592464637 1
 
< 0.1%
Other values (17510) 17510
99.9%
ValueCountFrequency (%)
-3.530084942 1
< 0.1%
-3.513148299 1
< 0.1%
-3.486995492 1
< 0.1%
-3.416376762 1
< 0.1%
-3.4046814 1
< 0.1%
-3.392922221 1
< 0.1%
-3.379406984 1
< 0.1%
-3.340786862 1
< 0.1%
-3.317218143 1
< 0.1%
-3.312932375 1
< 0.1%
ValueCountFrequency (%)
2.928105393 1
< 0.1%
2.880553072 1
< 0.1%
2.826195258 1
< 0.1%
2.784929869 1
< 0.1%
2.770634623 1
< 0.1%
2.750482358 1
< 0.1%
2.746342635 1
< 0.1%
2.740284463 1
< 0.1%
2.680043288 1
< 0.1%
2.67054547 1
< 0.1%

v_component_of_wind_100m
Real number (ℝ)

High correlation  Unique 

Distinct17520
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.082263 × 10-17
Minimum-3.8203308
Maximum3.4737449
Zeros0
Zeros (%)0.0%
Negative9263
Negative (%)52.9%
Memory size137.0 KiB
2025-10-05T00:04:13.789371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-3.8203308
5-th percentile-1.5228772
Q1-0.72665845
median-0.077689229
Q30.67716043
95-th percentile1.7610993
Maximum3.4737449
Range7.2940757
Interquartile range (IQR)1.4038189

Descriptive statistics

Standard deviation1.0000285
Coefficient of variation (CV)3.2444621 × 1016
Kurtosis-0.25665166
Mean3.082263 × 10-17
Median Absolute Deviation (MAD)0.69872129
Skewness0.26886989
Sum7.9580786 × 10-13
Variance1.0000571
MonotonicityNot monotonic
2025-10-05T00:04:13.852245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8151132311 1
 
< 0.1%
-0.3253514653 1
 
< 0.1%
-0.410021086 1
 
< 0.1%
-0.4914228796 1
 
< 0.1%
-0.506080206 1
 
< 0.1%
-0.1625172437 1
 
< 0.1%
-0.1079960161 1
 
< 0.1%
-0.214172475 1
 
< 0.1%
-0.2009380267 1
 
< 0.1%
-0.1478135733 1
 
< 0.1%
Other values (17510) 17510
99.9%
ValueCountFrequency (%)
-3.820330777 1
< 0.1%
-3.462557008 1
< 0.1%
-3.013227495 1
< 0.1%
-3.00350083 1
< 0.1%
-2.931127865 1
< 0.1%
-2.863065779 1
< 0.1%
-2.792895906 1
< 0.1%
-2.767522129 1
< 0.1%
-2.740341104 1
< 0.1%
-2.731200499 1
< 0.1%
ValueCountFrequency (%)
3.473744876 1
< 0.1%
3.384714257 1
< 0.1%
3.270466802 1
< 0.1%
3.254027447 1
< 0.1%
3.231496251 1
< 0.1%
3.216823619 1
< 0.1%
3.176846746 1
< 0.1%
3.072694422 1
< 0.1%
3.069428588 1
< 0.1%
3.038345267 1
< 0.1%

Interactions

2025-10-05T00:04:12.182636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:07.847562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:08.676855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:09.113580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:09.542405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:10.421135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:10.841884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:11.278539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:11.722147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:12.233376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:07.957611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:08.727569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:09.164860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:09.601658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:10.471119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:10.891868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:11.332397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:11.793952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:12.283122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:08.059464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:08.774029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:09.212966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:09.660221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:10.515057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:10.940356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:11.380427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:11.842941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:12.328767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:08.158475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:08.821280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:09.258692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:09.707140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:10.558742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:10.986323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:11.425762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:11.888874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:12.378859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:08.269368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:08.871036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:09.307849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:09.759192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:10.607702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:11.037249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:11.475226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:11.939655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:12.425906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:08.364185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:08.914598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:09.351825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:09.807608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:10.652594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:11.081057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:11.522838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:11.986634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:12.473109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:08.463432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:08.961362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:09.396035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:09.856007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:10.698661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:11.125901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:11.571367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:12.032919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:12.523803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:08.558658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:09.010275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:09.442399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:10.319428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:10.746074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:11.178460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:11.620891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:12.083107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:12.573774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:08.624544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:09.061346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:09.490243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:10.369307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:10.791982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:11.226753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:11.670063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-05T00:04:12.130606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-10-05T00:04:13.896917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
boundary_layer_heightdewpoint_temperature_2msurface_pressuresurface_solar_radiation_downwardstemperature_2mtotal_cloud_covertotal_precipitationu_component_of_wind_100mv_component_of_wind_100m
boundary_layer_height1.0000.185-0.2700.7560.5470.1830.2420.0450.075
dewpoint_temperature_2m0.1851.000-0.7510.0210.6560.4400.376-0.3850.415
surface_pressure-0.270-0.7511.000-0.052-0.772-0.330-0.2720.183-0.308
surface_solar_radiation_downwards0.7560.021-0.0521.0000.4340.0020.1050.1020.034
temperature_2m0.5470.656-0.7720.4341.0000.1990.171-0.0740.200
total_cloud_cover0.1830.440-0.3300.0020.1991.0000.479-0.3680.367
total_precipitation0.2420.376-0.2720.1050.1710.4791.000-0.2850.265
u_component_of_wind_100m0.045-0.3850.1830.102-0.074-0.368-0.2851.000-0.526
v_component_of_wind_100m0.0750.415-0.3080.0340.2000.3670.265-0.5261.000

Missing values

2025-10-05T00:04:12.640097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-05T00:04:12.714644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

boundary_layer_heightdewpoint_temperature_2msurface_pressuresurface_solar_radiation_downwardstemperature_2mtotal_cloud_covertotal_precipitationu_component_of_wind_100mv_component_of_wind_100m
0-0.663121-1.9415241.921340-0.743031-2.247569-0.868756-0.1980770.986786-0.325351
1-0.657830-1.9334111.974368-0.743031-2.262848-0.868756-0.1980770.998167-0.410021
2-0.656784-1.9271682.090291-0.736643-2.236287-0.855417-0.1980770.947828-0.491423
3-0.625887-1.9311272.175208-0.392288-2.220800-0.865396-0.1980770.807839-0.506080
4-0.538924-1.8021732.2383680.253750-2.058698-0.868756-0.1980770.447017-0.162517
5-0.314712-1.3121082.2868680.843736-1.347899-0.711146-0.1980790.300948-0.107996
60.144024-1.2952302.1957351.268286-1.109739-0.625033-0.1980770.349231-0.214172
70.348406-1.2920182.0739611.468810-1.029243-0.527378-0.1980770.336037-0.200938
80.476386-1.2935241.8171181.445775-0.715058-0.825496-0.1980770.259246-0.147814
90.569015-1.3958371.7411881.192150-0.639692-0.863429-0.1980770.156426-0.133550
boundary_layer_heightdewpoint_temperature_2msurface_pressuresurface_solar_radiation_downwardstemperature_2mtotal_cloud_covertotal_precipitationu_component_of_wind_100mv_component_of_wind_100m
17510-0.667083-1.2437741.360369-0.743031-1.589828-0.014761-0.198075-0.826529-0.205071
17511-0.688679-1.2678881.399951-0.743031-1.671231-0.401567-0.198081-0.7842030.019336
17512-0.704283-1.2789291.435469-0.743032-1.678653-0.551880-0.198077-0.6767180.238609
17513-0.715589-1.3112551.423517-0.743031-1.748990-0.731004-0.198077-0.5002640.454538
17514-0.719190-1.3350781.425837-0.743031-1.786521-0.810723-0.198077-0.2906270.633137
17515-0.717992-1.3688531.423455-0.743031-1.812320-0.801819-0.198077-0.1076340.597442
17516-0.712668-1.4170041.398877-0.743031-1.887070-0.819967-0.1980770.0621920.371275
17517-0.702256-1.4442421.394348-0.743031-1.916158-0.632846-0.1980810.2105390.048582
17518-0.687869-1.4841201.424105-0.743031-1.9529351.376508-0.1980770.332704-0.287206
17519-0.673855-1.5159201.395505-0.743031-1.9939471.737896-0.1980770.307168-0.815113